Identification of text regions in documents that are scanned (e.g. by an optical scanner of a printer) is significantly easier than detecting text regions in images of scenes in the real world (also called “natural images”) captured by a handheld device. FIG. 1A illustrates a billboard 100 in the real world in India. A user 110 may use a camera-equipped mobile device (such as a cellular phone) 108 to capture an image 107 of billboard 100. Captured image 107 may be displayed on a screen 106 of mobile device 108. Such an image 107 if processed directly by application of prior art techniques used in document processing may result in a failure to classify one or more portions as containing text (see FIG. 1A), e.g. caused by variations in lighting, color, tilt, focus, etc. Specifically, document processing techniques that are successfully used on scanned documents (during Optical Character Recognition, also called OCR) generate too many false positives and/or negatives, so as to be impractical for use on real world images.
Hence, detection of text regions in a real world image is performed using different techniques. For additional information on techniques used in the prior art, to identify text regions in natural images, see the following articles that are incorporated by reference herein in their entirety as background:    LI, et al. “Automatic Text Detection and Tracking in a Digital Video”, IEEE Transactions on Image Processing, January 2000, pages 147-156, Volume 9 No. 1;    LEE, et al. “A new methodology for gray-scale character segmentation and recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, October 1996, pages 1045-1050, vol. 18, no. 10;    EPSHTEIN, et al. “Detecting text in natural scenes with stroke width transform,” Computer Vision and Pattern Recognition (CVPR) 2010, pages 1-8, (as downloaded from “http://research.microsoft.com/pubs/149305/1509.pdf”).
When a natural image 107 (FIG. 1A) is processed to form blocks (such as block 103) of connected components or regions of interest, some prior art methods of the type described above are agnostic to skew (or orientation) of a word of text (see FIG. 1B) relative to a camera used to generate the image. However, some prior art methods are sensitive to skew, and may fail to correctly identify the block of text when the skew angle is large (e.g. 30° in FIG. 1B). So, there is a need to detect and correct skew in a natural image or video frame, prior to classification of regions, as described below.